Computing marginal distributions over continuous Markov networks for statistical relational learning

Conference Paper, Advances in Neural Information Processing Systems (NIPS), 2010, Vancouver, Canada
Authors: Matthias Broecheler and Lise Getoor
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Data set used in the experiments

Abstract
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general and present a polynomial-time approximation scheme under mild assumptions on the structure of the random field. Moreover, we introduce a sampling algorithm to compute marginal distributions and develop novel techniques to increase its efficency. Continuous MRFs are a general purpose probabilistic modeling tool and we demonstrate how they can be applied to statistical relational learning. On the problem of collective classification, we evaluate our algorithm and show that the standard deviation of marginals serves as a useful measure of confidence.

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